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Elementary Applications of Fourier Analysis

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Elementary Applications of Fourier Analysis ELEMENTARY APPLICATIONS OF FOURIER ANALYSIS COURTOIS Abstract. This paper is intended as a brief introduction to one of the very first applications of Fourier analysis: the study of heat conduction. We derive the steady state equation, which serves as our motivating PDE, and then iden- tify solutions to two fundamental scenarios: the first, a heat distribution on a circle; the second, one on a rod. In doing all of this there is much more room for depth that simply isn't explored for brevity's sake. A more comprehensive paper could explicitly define what constitutes a good kernel, the limitations of convergence of the Fourier transform, and different ways to circumvent these limitations. Theoretically, there's room to explore harmonic functions explic- itly, as well as Hilbert spaces and the L2 space. That would be the more analytic route to take; on the other hand there are many fascinating applica- tions that aren't broached either. In math, application is nearly synonymous with PDEs, and as far as partial differential equations are concerned, the most important feature that the Fourier transform has is the property of exchanging differentiation for multiplication. This property is really what makes bothering with the Fourier transforms of most functions worth it at all. Another equally important property of Fourier transforms is called scaling, which, in physics, directly leads to a concept called the Heisenberg Uncertainty Principle, which, like the whole of quantum physics, is very good at bothering people. Contents 1. Introduction 1 2. The heat equation 2 3. The Steady-state equation and the Laplacian 3 4. The Dirichlet Problem on the disc 3 5. Abel means and the Poisson kernel 5 6. The time-dependant heat equation on the real line 8 Acknowledgments 9 References 9 1. Introduction The mathematical field of Fourier Analysis was born out of the search for a general solution to the heat equation near the turn of the 19th century. Because we seek to explore several applications of Fourier Analysis, it will be necessary to redevelop the techniques and properties that combine to make this subject area illustrative and useful. As far as a starting point: there's no better place to start than the very problem that motivated this powerful technique - the heat equation. Date: July 21, 2009. 1 2 COURTOIS 2. The heat equation Consider an infinite metal plate which we model as the plane R2, and suppose we start with an initial heat distribution at time t = 0. Let the temperature at point (x; y) at time t = 0 be denoted by u(x; y; t). Consider a small square centered at (x0; y0) with sides parallel to the axis and of side length h, as shown in the picture below. The amount of heat energy in S at time t is given by ZZ H(t) = σ u(x; y; t) dx dy; S where σ is a constant called the specific heat of the material. Therefore, the heat flow into S is @H ZZ @u = σ dx dy; @t S @t @u ≈ σ h2 (x ; y ; t) @t o o (. as S has an area of h2.) Given two bodies with a common surface K, New- ton's law of cooling states that heat flows at a rate proportional to the difference in temperature between the bodies integrated over K. Note that κ is a proportionality constant named the conductivity. dH ZZ = κ ru · ^n dK; dt K For the vertical side on the right of our square S, this becomes @u −κh (x − h=2; y ; t); @x o o We can find the net rate of heat change on the square by adding up the other sides as well. ELEMENTARY APPLICATIONS OF FOURIER ANALYSIS 3 " dH @u @u =κh (x + h=2; y ; t) − (x − h=2; y ; t) dt @x o o @x o o # @u @u + (x ; y + h=2; t) − (x ; y − h=2; t) @x o o @x o o Then, by the mean value theorem and letting h tend to 0, we derive σ @u @2u @2u (2.1) = + κ @t @x2 @y2 a partial differential equation affectionately called the heat equation. 3. The Steady-state equation and the Laplacian If we wait long enough, the system will reach thermal equilibrium and the time derivative will reach zero. Our heat equation becomes @2u @2u (3.1) 0 = + @x2 @y2 This is called Laplace's equation. Laplace's equation is the specific case of the Laplacian of a function being equal to zero. The Laplacian is a differential operator, denoted by either r2 or ∆. For a function f, @2f @2f @2f @2f (3.2) ∆f = 2 + 2 + 2 + ··· + 2 @x1 @x2 @x3 @xn Laplace's equation, ∆f = 0, is a partial equation with solutions that are im- portant in many fields of science, such as electromagnetism, astronomy, and fluid dynamics. These solutions are important because they describe the behavior of electromagnetic, gravitational, and fluid potentials. In heat conduction specifically, Laplace's equation is called the steady state equation, representing a final heat distribution that is no longer changing, equation 3.1 above. Solutions of Laplace's equation are also of interest in pure mathematics. For L, an open subset of Rn, a harmonic function is a twice continuously differentiable function f : L ! R, that satisfies Laplace's equation, that is ∆f = 0. Harmonic analysis is a field of mathematics that studies various properties of these harmonic functions, as well as those of the Fourier series and the Fourier transform. 4. The Dirichlet Problem on the disc Consider the unit disc and its boundary, the unit circle: 2 (4.1) D = f(r; θ) 2 R : r < 1g; 2 (4.2) C = f(r; θ) 2 R : r = 1g The Dirichlet problem (for the Laplacian on the unit disc) concerns the search for solutions that satisfy the following conditions: i) u is a harmonic function on D, ii) u is equal to a scalar function f(θ) on C. 4 COURTOIS In the context of heat conduction, this involves fixing a predetermined temper- ature distribution on the unit circle, waiting a long time, and then examining the final distribution of temperature. final.png The boundary, f(θ), is not easily expressible in Cartesian coordinates and so it's helpful to express the Laplacian in terms of polar coordinates. This is done by an application of the chain rule for partial derivatives. The end result: @2u 1 @u 1 @2u ∆u = 0 = + + @r2 r @r r2 @θ2 Rearranging the equation and multiplying by r2, @2u @2u @u − = r2 + r @θ2 @r2 @r We now separate variables and look for a solution of the form u(r; θ) = F (r)G(θ). We find: r2F 00(r) + rF 0(r) G00(θ) = − F (r) G(θ) Because each side depends on a different variable, they must each be equal to a constant, call this λ. G00(θ) + λG(θ) = 0 r2F 00(r) + rF 0(r) − λF (r) = 0 Functions sin(θ), cos(θ), and eiθ are all valid solutions for G. Because Laplace's equation is linear, a full solution can be expressed as a superimposition of all possible solutions and, indeed, this sum for G(θ) is ELEMENTARY APPLICATIONS OF FOURIER ANALYSIS 5 G(θ) = An cos(nθ) + Bn sin(nθ); n 2 Z 1 X inθ (4.3) ane n=−∞ The only solution for F (r) defined at the origin turns out to be F (r) = rjnj. Therefore, we are left with the following solution: 1 X jnj inθ (4.4) u(r; θ) = anr e n=−∞ This is not just a Fourier series, but in fact the Abel means of our function u. 5. Abel means and the Poisson kernel Fourier series are very delicate creatures. Given functions that are too unruly, the Fourier sum may not converge in places. Sn(f)(θ) 6= f(θ) Figure 1. castastrophe! For people like you and I who are interested in expressing functions in terms of their Fourier series, this can be a very bad thing. If we add the constraint that our functions be of the class C2, our Fourier series will naturally uniformly converge everywhere. That said, the more accommodating the domain of an operation, the stronger it's generally considered. The simple Fourier sum of a function f is of the form 1 X inθ Sn(f)(θ) = ane ; n=−∞ We can extend the class of functions whose Fourier series converge by looking at our sums in terms of Abel summation. A sum is Abel summable to s if for 0 ≤ r < 1, 1 X n An(r; θ) = anr converges and lim A(r) = s r!1 n=0 By looking at our Fourier series in terms of Abel summability, we can guarantee pointwise convergence of Fourier series anywhere a function is continuous. Let's play around with our Abel sum! 1 X jnj inθ Ar(f)(θ) = r ane n=−∞ 1 X 1 Z π = rjnj f(')e−in' d' einθ 2π n=−∞ −π 1 ! 1 Z π X (5.1) = f(') rjnje−in('−θ) d' 2π −π n=−∞ 6 COURTOIS This form of Abel summation becomes particularly illustrative if we introduce a new operation. For two functions r(t) and s(t), their convolution, denoted (r ∗s)(t) is defined: Z 1 (r ∗ s)(t) = r(τ) · s(t − τ) dτ 1 Convolution is a binary operation that performs a sort of \sliding integral of two functions." The feature of convolutions that currently interests us is called approximation to the identity. Given a function f and a good kernel Kn, lim (f ∗ Kn)(x) = f(x) n!1 wherever f is continuous.
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